The field of immuno-oncology (IO) has rapidly expanded in recent years, with the development of immunotherapies such as immune checkpoint inhibitors and CAR T cell therapy.

These therapies have shown promising results in the treatment of various types of cancer, ranging from melanoma and lung cancer to hematologic malignancies. However, not all patients respond to these therapies, and identifying biomarkers that predict response to IO is a key challenge in the field.

Tumor pathology is also an important aspect of IO, as it can impact the immune response and potentially modulate the efficacy of immunotherapy. In this article, we will discuss the significance of biomarkers and tumor pathology in IO.

Types of Biomarkers

Biomarkers are measurable indicators of biological processes, and can be used to predict disease prognosis, monitor disease progression, and guide treatment decisions.

In the context of IO, biomarkers are particularly important for predicting response to therapy. There are several types of biomarkers that have been studied in the context of IO:.

Tumor Mutational Burden

Tumor mutational burden (TMB) is a measure of the number of mutations present in a tumor.

High TMB has been associated with improved response to immune checkpoint inhibitors, possibly because increased mutation leads to the generation of neoantigens that can be recognized by T cells. TMB is currently being studied as a potential biomarker for predicting response to IO.

Microsatellite Instability

Microsatellite instability (MSI) is a DNA replication error that leads to the accumulation of mutations in microsatellite sequences.

MSI-high tumors have been shown to have improved response to immune checkpoint inhibitors, possibly because of increased neoantigen generation. MSI status is currently used as a biomarker for selecting patients for pembrolizumab therapy in colorectal and endometrial cancer.

PD-L1 Expression

PD-L1 is a protein that is often upregulated on tumor cells in response to immune-mediated inflammation. It interacts with its receptor, PD-1, on T cells and suppresses the T cell response.

PD-L1 expression has been shown to be a predictor of response to immune checkpoint inhibitors in several cancer types, including non-small cell lung cancer and melanoma. However, it is not a perfect biomarker, as some patients with low PD-L1 expression may still respond to therapy.

Tumor-Infiltrating Lymphocytes

Tumor-infiltrating lymphocytes (TILs) are T cells that have infiltrated into the tumor microenvironment.

The presence of TILs has been associated with improved response to immune checkpoint inhibitors in several cancer types, including melanoma and non-small cell lung cancer. TILs are currently being studied as a potential biomarker for predicting response to IO.

Biomarkers in Combination

While each biomarker has some predictive value on its own, combining multiple biomarkers may improve prediction of response to IO.

For example, in a study of patients with metastatic melanoma, a combination of TMB and TILs was a better predictor of response to immune checkpoint inhibitors than either biomarker alone.

Tumor Pathology

Tumor pathology can also impact response to IO by influencing the immune microenvironment of the tumor.

For example, the presence of tumor-associated macrophages (TAMs) has been associated with a poor prognosis in several cancer types, including breast and ovarian cancer. TAMs can suppress the immune response by secreting immunosuppressive cytokines and inhibiting T cell function. Targeting TAMs with immunotherapy may therefore be an attractive approach to enhancing the efficacy of IO.

Tumor-Intrinsic Factors

Tumor-intrinsic factors, such as the presence of oncogenic mutations, can also impact response to IO. For example, patients with BRAF-mutant melanoma may have a lower response rate to immune checkpoint inhibitors than patients with wild-type BRAF.

This may be because BRAF mutations can lead to alterations in the tumor microenvironment that suppress the immune response. Strategies to overcome these tumor-intrinsic barriers to immune response are currently being investigated.

Conclusion

In conclusion, biomarkers and tumor pathology are important considerations in the field of IO. Identifying effective biomarkers to predict response to therapy is a key challenge, but combining multiple biomarkers may improve prediction accuracy.

Tumor pathology can also impact response to IO by influencing the immune microenvironment of the tumor, and targeting these factors with immunotherapy may be a promising approach to enhancing the efficacy of IO.